Jian Qing Shi, Taeryon Choi

Gaussian Process Regression Analysis for Functional Data

• Gebundenes Buch

Produktbeschreibung

A Gaussian process is a type of stochastic process that is particularly useful for Bayesian inference of complex problems. This book presents an overview of Gaussian process regression, all of the necessary theory, and real-world applications from the fields of medicine and engineering. It covers the required background in regression and examines key aspects of the modeling process, including model selection, as well as more advanced topics, such as mixture models and kernel-based methods. For implementing the methods discussed in the text, the authors provide ad hoc software for download on their website.

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Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables. Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dimensional data and variable selection. The remainder of the text explores advanced topics of functional regression analysis, including novel nonparametric statistical methods for curve prediction, curve clustering, functional ANOVA, and functional regression analysis of batch data, repeated curves, and non-Gaussian data. Many flexible models based on Gaussian processes provide efficient ways of model learning, interpreting model structure, and carrying out inference, particularly when dealing with large dimensional functional data. This book shows how to use these Gaussian process regression models in the analysis of functional data. Some MATLAB® and C codes are available on the first author's website.

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Produktdetails

• Verlag: Chapman and Hall/CRC
• Seitenzahl: 218
• Erscheinungstermin: 1. Juli 2011
• Englisch
• Abmessung: 240mm x 161mm x 16mm
• Gewicht: 496g
• ISBN-13: 9781439837733
• ISBN-10: 1439837732
• Artikelnr.: 32404994

Autorenporträt

Jian Qing Shi, Ph.D., is a senior lecturer in statistics and the leader of the Applied Statistics and Probability Group at Newcastle University. He is a fellow of the Royal Statistical Society and associate editor of the Journal of the Royal Statistical Society (Series C). His research interests encompass functional data analysis using covariance kernel, incomplete data and model uncertainty, and covariance structural analysis and latent variable models. Taeryon Choi, Ph.D., is an associate professor of statistics at Korea University. His research mainly focuses on the use of Bayesian methods and theory for various scientific problems.

Inhaltsangabe

Introduction. Bayesian Nonlinear Regression with Gaussian Process Priors.
Inference and Computation for Gaussian Process Regression Model. Covariance
Function and Model Selection. Functional Regression Analysis. Mixture
Models and Curve Clustering. Generalized Gaussian Process Regression for
Non-Gaussian Functional Data. Some Other Related Models. Appendices.
Bibliography. Index.